swiss_roll: Swiss Roll Data Set

In jlmelville/snedata: SNE Simulation Dataset Functions

Swiss Roll Data Set

Description

Simulation data randomly sampled from a 2D plane curled up into 3 dimensions.

Usage

swiss_roll(n = 1000, min_phi = 1.5 * pi, max_phi = 4.5 * pi, max_z = 10)

Arguments

n	Number of points to create.
min_phi	Minimum value of the range of phi to sample from
max_phi	Maximum value of the range of phi to sample from.
max_z	Maximum value of the z range to sample from (minimum values is always 0).

Details

Creates a series of points randomly sampled from a swiss roll-shaped manifold: a two-dimensional plane which has been rolled up into a spiral shape. Or just look at a swiss roll.

The formula for sampling the x, y and z coordinates used in this dataset is from that given in the Stochastic Proximity Embedding paper by Agrafiotis and Xu (I don't know who originally came up with the data set, though):

x = \phi cos\phi, y = \phi sin \phi, z

where \phi and z are random numbers in the intervals [\frac{3\pi}{2}, \frac{5\pi}{2}] and [0, 10], respectively (the range of \phi and z can be modified, if desired).

Points are colored based on the value of phi. If you unrolled the manifold into a flat sheet, you would see the color change linearly in the direction you unrolled it. Or just plot the x-y cross section (see the examples).

Value

Data frame with x, y, z columns containing the coordinates of the points and color the RGB color.

References

I first saw the equations in

Agrafiotis, D. K., & Xu, H. (2002). A self-organizing principle for learning nonlinear manifolds. Proceedings of the National Academy of Sciences, 99(25), 15869-15872.

But the dataset turns up everywhere, most notably:

Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323-2326.

If the idea of flattening a Swiss Roll didn't originate in that publication, it was certainly popularized for its use in nonlinear dimensionality reduction. A Matlab-formatted version of that dataset is still available at:

http://web.mit.edu/cocosci/isomap/datasets.html

I'm not sure exactly what parameters were used to generate it, but you can get something similar by calling: swiss_roll(n = 20000, min_phi = 1.5 * pi, max_phi = 4.5 * pi, max_z = 50)

Examples

## Not run: 
swiss1000 <- swiss_roll(n = 1000)
# Coloring should be obvious with a 2D plot of the cross section:
plot(swiss1000$x, swiss1000$y, col = swiss1000$color, pch = 20)

## End(Not run)

jlmelville/snedata documentation built on Jan. 13, 2024, 2:06 a.m.